Enrolment forecasting, which provides information for decision making and budget planning, is important in many ways to higher education. Because of its importance, researchers have proposed many forecasting methods to improve accuracy. Different methods such as genetic algorithm, least square that are used to forecast enrolment of student do not give relatively accurate results. However, obtaining accuracy is not an easy task, as many factors have impacts on enrolment numbers. In this work, a fuzzy time series was developed for efficient enrolment forecasting. The model is made up of four steps which are definition of the universe of discourse and intervals, fuzzification of historical data, establishment of fuzzy relationships and enrolment forecast. The max-min operator was used as universe of discourse and we compared our proposed method with the existing linear regression method. The historical enrolment figures of the University of Agriculture, Abeokuta were used as a data set for testing and were implemented using Visual Basic. The forecasting result of the fuzzy time series method is compared with that of the existing least square method, the fuzzy time series method produces the smallest values of the mean square error (MSE) as compared with the least square method. The application was also used to predict students’ enrolment for the next five years. The proposed method was found to obtain more accurate forecasting results than the existing method.


Enrolment, Forecast, Fuzzy Time Series, Least Square Method, Universe of Discourse

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Chen, S. M. 1996. Forecasting Enrolments based on Fuzzy Time Series. Fuzzy Sets and Systems, 81: 311-319.

Chen, S., Hsu, C. 2004. A new method to forecast enrolments using fuzzy time series, International Journal of Applied Science and Engineering, 2(3): 234-244.

Chen, S., Chung, N. 2006. Forecasting Enrolments of Students by Using Fuzzy Time Series and Genetic Algorithms, Information and Management Sciences, 17( 3): 1-17.

Chen, S.M. Hwang J.R. 2000. Temperature prediction using fuzzy time series, IEEE Transactions on Systems, man and Cybernetics-part B: Cybernetics, 30(2): 263-275.

Chen S.M. 2002. Forecasting enrolments based on high-order fuzzy time series, Cybernetics and Systems: an International Journal, 33(1): 1-16.

Damousis, I.G., Dokopoulos, P. 2001. A fuzzy expert system for the forecasting of wind speed and power generation in wind farms, proceedings of the second IEEE International conference on power industry computer applications, Sydney Australia, 63-69.

Jilani, T.A., Burney, S.M.A. 2007a. M- factor high order fuzzy time series forecasting for road accident data, IEEE-IFSA, 2007, World congress. Cancun, Mexico. 18-21.

Jilani, T.A., Burney, S.M.A 2007b. Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning, World Academy of Science, Engineering and Technology, 34.

Park, S., Lee-Kwang, H. 2001. A designing method for type-2 fuzzy logic systems using genetic algorithms, Proceedings of the joint 9th IFSA World Congress and 20th NAFIPS International Congress, Vancouver, Canada, 5: 2567-2572.

Song, Q., Chisson, B.S. 1991. Forecasting enrolments with fuzzy time series, Paper Presented at the Annual Meeting of the Mid-South Educational Research Association, 20th, Lexington, KY, 12-15.

Stevenson, M., Porter, J.E. 2009. Fuzzy time series forecasting using percentage change as the universe of discourse, World Academy of Science, Engineering and Technology, 55.


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